Masanori Shimono

The paper develops a Transformer model to generate and predict neuronal spike data across in vitro and in vivo domains using a Dice objective to improve performance and transferability.

This paper introduces a neural network embedding method to compress and interpret functional connectivities in mouse brain data, showing improved interpretability with combined automated and metric-based analysis.

This study evaluates microconnectomes across 16 cortical regions, finding unique characteristics in the frontal group that may explain its role in generating global brain activity patterns.

This study uses a multi-layer LSTM model to generate neuronal spike activity between disconnected brain regions, showing similar accuracy to within-region generation and revealing associations with structural connectivity and distance.

The paper shows inhibitory neurons have central positions and stronger control in cortical microconnectomes, maintaining brain activity balance.

The paper shows that communicability, a graph-theoretic measure, captures propagation latencies in brain signals across different conscious states.

This paper introduces a TMS-EEG technique to measure individual differences in perception by tracking resting-state gamma-band effective connectivity during a bistable stimulus task.